Compressive imaging enables faster acquisitions by capturing coded projections of the scenes. Codification elements used in compressive imaging systems include lithographic masks, gratings and micro-polarizers, which sense spatial, spectral, and temporal data. Codification plays a key role in compressive imaging as it determines the number of projections needed for correct reconstruction. In general, random coding patterns are sufficient for accurate reconstruction. Still, more recent studies have shown that code design not only yields to improved image reconstructions, but it can also reduce the number of required projections. This talk covers different tools for codification design in compressive imaging, such as the restricted isometry property, geometric and deep learning approaches. Applications in compressive spectral video, compressive X-ray computed tomography, and seismic acquisition will be also discussed.